For sake of consistent comparison, we restrict our baselines to methods of the same class, that is, \(l_0\)  regularized or constrained sparse regression algorithms. This notably excludes \(l_1\)  regularized sparse regression methods like LASSO and its variants [1]}, [2]}, probabilistic methods, and deep learning based approaches. Specifically, we compare our MIO based approach (MIOSR) to four common optimizers in the SINDy literature: sequential threshold least squares (STLSQ) [3]}, sparse relaxed regularized regression (SR3) [4]}, stepwise sparse regression (SSR) [5]}, and ensembling using STLSQ (E-STLSQ) [6]}.
